A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
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BaySC introduces an integrative Bayesian spatial clustering model with MFM prior for automatic domain count, MRF for local coherence, and weighted log-likelihood fusion for multi-omics data, validated on twelve datasets with competitive metrics and better spARI.
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Latent Impact and Differential Item Functioning Analysis for Asymmetric IRT Models
A new ℓ1-regularized mixture asymmetric IRT framework jointly recovers latent classes for impact and selects DIF items without group labels or anchors, as shown in simulations and two educational datasets.
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BaySC: Uncovering Tissue Architecture in Spatial Multi-Omics via Probabilistic Spatial Clustering
BaySC introduces an integrative Bayesian spatial clustering model with MFM prior for automatic domain count, MRF for local coherence, and weighted log-likelihood fusion for multi-omics data, validated on twelve datasets with competitive metrics and better spARI.